Background: The phenomenon of "empty nest youth" is becoming increasingly ubiquitous, capturing the attention of society at large. However, few studies have been conducted in recent years on this group, especially focusing on their family and mental health. As such, this study investigates the correlation between family health and well-being among "empty nest youth," as well as the function of social support and loneliness in this relationship. Methods: A cross-sectional survey was conducted from June to August 2022 across 32 provinces, municipalities, and autonomous regions in China, utilizing a multi-stage sampling technique. And we screened individuals who were unmarried, living alone, and between 22-44 years old, resulting in a valid sample size of 908 cases; multiple regression analysis, mediation effect testing, and moderation effect testing are used to examine research hypotheses. Results: The regression analysis results show that family health not only has a direct impact on well-being (β = 0.36, p < 0.001) but also indirectly affects well-being through social support [β = 0.23, 95% CI: 0.19 0.28]. Additionally, the loneliness moderates the predictive impact of not only family health on social support (β = -0.13, p < 0.001) but also social support on well-being (β = -0.06, p < 0.001). Conclusions: These findings underscore the significance of directing policymakers and healthcare professionals towards the "empty nest youth's" familial and social support systems. It underscores the need for the development of policies aimed at addressing their emotional and material requirements by leveraging these familial and social networks. This approach ultimately contributes to the enhancement of their overall psychological well-being, promoting a more coherent and logical pathway for intervention and support.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.

Nowadays, graphs including bigraphs are mostly used in various real-world applications such as search engines and social networks. The bigraph or bipartite graph is a graph whose vertex set is split into two disjoint vertex sets such that there is no edge between the same vertex set. The bipartite graphs are colored using only two colors. This article checks if a given graph is bipartite or not and finds the color assignments of the bipartite graph using Java implementation.